Modelling Project Success by Alfi, Jiaying, Himanshu, Sara
Problem Solving Strategy¶
The ideal machine learning project involves general flow analysis stages for building a Predicting Model. Steps followed to perform data analysis:
1. Understanding the problem domain
2. Data Exploration and Preparation
3. Feature Extraction
4. Dimensionality Reduction (or Feature Selection)
5. Various Model Evaluation and
6. Hyper-parameter Tuning
7. Ensembling: Model Selection
Each of these stages will be followed throughout this Predictive Model Design.¶
STEP 1. Understanding the problem domain¶
Kickstarter - Crowfunding and Success¶
Kickstarter Is an American public-benefit corporation based in Brooklyn, New York, that maintains a global crowdfunding platform focused on creativity The company's stated mission is to "help bring creative projects to life". Kickstarter has reportedly received more than $1.9 billion in pledges from 9.4 million backers to fund 257,000 creative projects, such as films, music, stage shows, comics, journalism, video games, technology and food-related projects. People who back Kickstarter projects are offered tangible rewards or experiences in exchange for their pledges. This model traces its roots to subscription model of arts patronage, where artists would go directly to their audiences to fund their work.
Project Owner's Perspective:
- What is an ideal and optimal range of the funding goal for my project ?
- On which day of the week, I should post the project on Kickstarter ?
- How many keywords should I use in my project title ?
- What should be the total length of my project description ?
Kickstarters Perspective: A large amount of manual effort is required to screen the project before it is approved to be hosted on the platform. Key ingredients for the project to be successfull.
why not a model to predict if a project will be successful before it is released?¶
List of possible predicting factors:
- Total amount to be raised - More amount may decrease the chances that the project will be successful.
- Total duration of the project - It is possible that projects which are active for very short or very long time periods are not successful.
- Theme of the project - People may consider donating to a project which has a good cause or a good theme.
- Writing style of the project description - If the message is not very clear, the project may not get complete funding.
- Length of the project description - Very long piecies of text may not perform good as compared to shorter crisp texts.
- Project launch time - A project launched on weekdays as compared to weekends or holidays may not get complete funding amount.
Given Dataset¶
Independent-¶
ID:¶
- ID 378661 non-null int64 -- Unique project Id
Text:¶
- name 378657 non-null object -- Project name
- main_category 378661 non-null object -- Main category - Food/Music/Video
- category 378661 non-null object -- Sub-category Type of Industry, For ex- Restaurant, Food, Poetry, Product Design
Date:¶
- deadline 378661 non-null object -- Crowd Funding Dead line
- launched 378661 non-null object -- date launched
Categorical: Nominal¶
- currency 378661 non-null object -- Type of Currency
- country 378661 non-null object -- Country
Numerical:¶
- goal 378661 non-null float64 -- Goal - The amount of money creator needs to complete the project
- pledged 378661 non-null float64 -- amount pledged by crowd
- backers 378661 non-null int64 -- number of supporters
- usd pledged 374864 non-null float64 -- Pledged amount in USD (conversion made by KS)
- usd_pledged_real 378661 non-null float64 -- Pledged amount in USD (conversion made by fixer.io api)
- usd_goal_real 378661 non-null float64 -- Goal amount in USD
Dependent- Nominal¶
- state 378661 non-null object -- Project Status - Successfull, failed, canceled, undefined, etc...
STEP 2. Data Exploration and Preparation¶
Data Cleaning and Noise Removal¶
- Verify Individual distinct column values
- Get rid of unwanted columns (active stage columns)
- Remove Duplicates if exist
- Handle Missing Values, in this case Delete those rows
- Get rid of noise above 2200000 goal amount (all failed)
- Project launched during 1970 and 2018 (6 rows) can be removed
- Misrepresented data such as "N,0"" in country column must be addressed, it will be cleaned as a part of data cleaning.
Columns which is not useful for analysis are as follows and can be removed: ID, goal, pledged, usd_pledged and currency
Cancelled State - There are 10% of projects in this dataset are in cancelled state. Based on business logic it could imply as failed as the project owner at some point while campaign is live, figured it will not work and cancelled the campaing. There are several other resons as well.
For Example, Project owner got funding from somewhere else or the project requirements changed which let him recreate online crowd funding campaign.
Since there is no clear reason given in this dataset for Project to get cancelled or no date on which it got cancelled. here, Canceled state should be considered as separate state and not failed.
Data Exploration - Scatter Plot for Goal vs Pledged Amount¶
Observations from the scatter plot presents:
- So many projects which are successfull had goal below USD 1 millions.
- Most Projects successful raised more than planned goal.
- After approximate USD 2 mllions goal, none of those projects were successfull.
Additionally, There is about 13 % of the projects which have not raised single penny and are either cancelled or failed.
Be noted: 1e8 is standard scientific notion, and here it indicates an overall scale factor for the y-axis. That is, if there's a 2 on the y-axis and a 1e8 at the top, the value at 2 actually indicates 21e8 = 2e8 = 2 10^8 = 200,000,000.
Distributions - Outliers and Skew¶
A general guideline for skewness is that if the number is greater than +1 or lower than –1, this is an indication of a substantially skewed distribution. For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked. Likewise, a kurtosis of less than –1 indicates a distribution that is too flat.
Interpreting Skewness: If skewness is less than −1 or greater than +1, the distribution is highly skewed. If skewness is between −1 and −½ or between +½ and +1, the distribution is moderately skewed. If skewness is between −½ and +½, the distribution is approximately symmetric.
Numeric variables such as backers, usd_pledged_real, usd_goal_real are higly right skewed because of so many failed instances not having single backers or pledged amount raised. This will be addressed through data normalization while developing a model.
To explore these data it needs to be transformed and then histogram should be created to visualize distributions.